👀 Views: 16 💬 Answers: 1 📅 Created: 2025-06-14

apache-spark dataframe performance python

I'm a bit lost with I've searched everywhere and can't find a clear answer... I'm currently facing an issue with Apache Spark 3.4.1 where I am trying to leverage DataFrame caching to improve the performance of my iterative machine learning pipeline... However, I'm noticing some unexpected behavior with lazy evaluation that seems to be affecting the results of my computations. I have a DataFrame `df` that I am transforming multiple times through a series of filter and transformation operations. To optimize the process, I decided to cache the DataFrame: ```python from pyspark.sql import SparkSession spark = SparkSession.builder.appName("CachingExample").getOrCreate() df = spark.read.parquet("path/to/data.parquet") df.cache() ``` After caching, I perform a series of transformations: ```python filtered_df = df.filter(df['column'] > 100) transformed_df = filtered_df.withColumn('new_column', filtered_df['column'] * 2) final_df = transformed_df.select('new_column') ``` When I call `final_df.show()`, I expect to see the results based on the transformations on the cached DataFrame. However, I still see queries being executed on the original DataFrame instead of the cached version, which leads to longer execution times and, more importantly, some discrepancies in the data that appears in my results. I also noticed that if I explicitly trigger an action like `df.count()` before `final_df.show()`, the expected behavior occurs, but that feels counterintuitive. I tried setting the spark configuration `spark.sql.cache.enable` to `true` (although it's true by default) in case that might influence caching behavior. Is this expected behavior in Spark? How can I ensure that my cached DataFrame is used throughout my transformations without needing to trigger an action on the original DataFrame? Any advice or insights would be greatly appreciated. I'm working on a application that needs to handle this. For context: I'm using Python on Windows. I'm using Python 3.11 in this project.